Hiking trail maps are typically created manually by survey, a time-consuming process. This process is expensive and must be repeated over time to improve accuracy. This paper proposed an inexpensive, automatic, and accurate trail network generation method from anonymous public GPS data utilizing a growing self-organizing map (GSOM). This technique does not rely on sequential GPS traces to learn network topology, unlike other approaches. Tuning several hyper-parameters can adjust this process for datasets and networks with unique characteristics. Reconstruction and adaption are also possible based on newly acquired data sources. Constructed trail maps, trained on publicly available GPS data, are compared against a ground truth map from Open Street Map (OSM). Performance is evaluated based on completeness, accuracy, and topological correctness. Testing on sparse networks with minimal GPS noise suggests favorable performance.
The Mississippi Delta encompasses 3.0 million acres, of which 2.2 million are irrigated for staple crops such as corn, soybeans, rice, and catfish. The Mississippi River Valley Alluvial Aquifer (MRVAA) is used to irrigate this area; over the past 30 years, it has decreased 20 feet in depth, requiring efforts to increase aquifer recharge and surface water availability for agriculture demands. A weir constructed in a river pools water upstream, promoting aquifer recharge through infiltration and increasing the availability of surface water withdrawals. Weir placement is important to the success of aquifer recharge. In partnership with the Yazoo Mississippi Delta Joint Water Management District (YMD), we created a scalable weir design guide that will allow for broad implementation and increased water availability across the Delta region. The site for the first weir was selected as an area characteristic of the Delta region and will serve as an example for subsequent designs. The supplemental design guide will allow for further weir implementation in the Delta region by providing the necessary steps for a weir design process. Inputs will be easily accessible for non-engineers and the outputs will be designs for Professional Engineer approval. By providing YMD with this weir guide, they will be able to design and install weirs more quickly and cheaply than before, supporting agriculture and groundwater resources in the Mississippi Delta.
The objective of the Car Wash Project is to prevent people from using self serve open wash bays for long periods of time without payment, which costs car wash owners considerable revenue. The solution proposed by the team is designing a universal car wash deterrent system that can be customized to multiple different car wash designs and bay layouts. The deliverable is an installation kit that can be shipped and self-installed. The packaged kit will include an ultrasonic vehicle-detection sensor, an LED strobe light, an LED display board, and a water solenoid valve. If customers stay too long without paying, the strobe light will light up, and after a set countdown, the water valve will open and the sprinklers will turn on.Everything needed for the system will be included in the installation kit other than piping, cables, and wires. The team has successfully combined all the separate components into one operational circuit as well as getting the most suitable products for the device so it can run at optimal efficiency. We will display the installation kit, how the system operates, along with a schematic of electronics.
Early cancer detection remains an important problem in healthcare today. Since the mid-1990s, Optical Coherence Tomography (OCT) has been explored as a cancer detection instrument. Previous studies have shown connections between tissue porosity, cell behavior, cell topology, and their relation to cancer and disease progression. Previous researchers have found that in a healthy cell, the pore size of the surrounding extracellular matrix (ECM) is homogenous. However, in a cancerous cell, heterogeneous pores appear. Additionally, as cancer progresses, pore size decreases. Herein, we propose a new method of improving cancer detection using OCT. In a study utilizing an artificial ECM, a connection between pore size and gold nanorod (GNR) diffusion was established such that smaller pores lead to less diffusion, and vice versa. Cell behavior is measured by cell motility, which refers to the rapid, in-place motions of intracellular parts that can be used to assess cell response to therapy, their surrounding environment, and potentially reveal premalignant behavior. Previous investigators have defined two metrics of cell movement, alpha, and motility, which correspond to signal auto-decorrelation and signal amplitude, respectively. Cell topology refers to the 3D structure and shape of cells and cell clusters, which has been shown to mutate in diseases such as cancer. By quantifying cell topology, cellular health can be examined. Techniques using OCT have also been used to monitor the response of diseased tissue to treatment. These studies have been largely independent of each other, and the need for a more holistic measuring system has been called for. This research aims to create a custom OCT system capable of obtaining these metrics simultaneously and with improved imaging depth and comparable resolution. Through an integration of a near-infrared (NIR) laser, interferometer, and LabVIEW control of the system, a new Deep-Imaging, Multi-Parameter OCT (DIMP-OCT) is being created. The system bodes a 4.6µm resolution and 5.4mm imaging depth. This is made possible by a 50-50 fiber optic beam splitter using a 1300nm wavelength laser with 160nm bandwidth, and 2048-pixel spectrometer with a 140kHz linerate. Here, we report the design of the system being built, the techniques used to build and test the hardware, and the approach to developing the graphical user interface. We also will report results from tests to assess DIMP-OCT subsystems.
This paper details an approach to identify multiple-choice questions that are most effective in discriminating deficiencies in mathematics competencies of incoming first-year engineering students who are graduates of the K-12 program that was recently implemented in the Philippines. To achieve this objective, machine learning algorithms such as the k-Nearest Neighbors (kNN), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Gradient Boosting Machines (GBM) were implemented. From a question bank containing 1,300 questions covering Algebra (A), Advanced Algebra (AA), Plane and Spherical Trigonometry (T), Analytic Geometry (AG), and Solid Mensuration (SM), five domain experts identified the suitability of the questions as part of a diagnostic examination in mathematics. Specifically, using a 5-point Likert scale (5 being the highest), the experts rated the suitability of each question to test the proficiency of a student in 23 mathematics competencies based on what is prescribed by the Commission on Higher Education (CHED). The collected survey data were then used to train the machine learning models, which extracted patterns to identify the questions that would be most suitable to test the mathematics competencies of incoming first-year engineering students. With a precision recall score of 99.90%, the LR model was selected as the best performing model and analysis of how the LR model predicts the labels through the use of shap values revealed that the preference was given towards questions which test student proficiency in foundational mathematics competencies like that of Algebra and Analytical Geometry. Overall, these findings provided a better understanding of the questions that are most effective in discriminating student deficiencies in mathematics subjects.
The quality of a product or process is an important issue for both customers and producers. The quality could be defined as a linear relationship between the response variable (s) and explanatory variable (s), which is called a linear profile. Another essential concept in quality control is the adaptation of quality specifications with customers' standards. The proper tool to measure customers' specifications is process capability indices (PCIs). To find the PCIs for profiles, the profile parameters should be estimated. These parameters can be estimated using classic estimators. However, in the presence of outliers, the classic estimators do not estimate the parameters accurately. Therefore, the performance of the classic indices using classic estimators is appropriate only in the absence of contamination. In this research, robust estimate methods such as M-estimator and LR-weighted MCD estimators are used to propose robust PCIs for multivariate linear profiles. The proposed robust indices include C pm and MC pc for a multivariate linear model. The performance of the proposed robust PCIs is compared with the classic PCIs in the absence and presence of contamination. The result of simulation studies shows that robust PCIs perform better than classic PCIs in the presence of outliers. In the absence of contamination, the robust PCIs perform as accurately as classic PCIs. The proposed PCIs using LR-weighted MCD outperform the M-estimator method in all considered contamination scenarios.
There is unequivocal evidence that the Earth is warming at an unprecedented rate, and that the burning of fossil fuels is the principal cause. This situation is fostering a growing interest in shifting global energy production toward renewable energy sources such as solar, wind, and hydropower. Hydropower plays an important role in meeting global carbon mitigation targets and eventually achieving net-zero carbon emissions, especially within the Mid-Columbia (Mid-C) energy market in the Pacific Northwest (PNW), where hydropower currently comprises 50-65% of its generation. However, other renewable energy sources in the Mid-C market and connected California Independent System Operator (CAISO) power grid are expanding significantly, particularly solar power in California (CA). Thus, hydropower operations at plants within the connected Mid-C market may need to be re-operated to balance the more intermittent supply from renewables in CA so that energy supplies are in phase with demands. In this study, our goal is to re-design hydropower operations in the Columbia River Basin (CRB) of the PNW to achieve a 95% renewable energy power grid in CA and the PNW by the year 2035. This will require not only filling supply gaps from other renewable energy sources, but also balancing other conflicting objectives to be fulfilled by the dam operations, such as minimizing environmental spill violations, maximizing hydropower production, maximizing flood protection, and maximizing economic benefits. We use multi-objective optimization to design alternative operations at four CRB dams to balance these objectives over the historical record. We then simulate their operations over alternative possible future climate change and energy development scenarios to find a recommended set of operations that are robust to these uncertainties. The energy scenarios include the National Renewable Energy Lab’s (NREL) Mid-Case Energy Scenario for the years 2025, 2030 and 2035, which achieve 95% Renewables by 2035, as well as a business as usual (BAU), or base case, scenario represented by the historical energy mix. The four climate scenarios are made from combinations of low or high warming and low or high streamflow for three overlapping time steps: 2020-2029, 2025-2034, and 2030-2039. Our optimization is able to find a robust compromise policy that balances the system’s conflicting objectives well both now and in the future. We close by exploring how this policy coordinates operations across system reservoirs, which could inform reservoir operators in the CRB about how to adapt operations as the system changes in the future.
Large public transportation systems, like that of the Washington Metropolitan Area Transit Authority (WMATA), must appropriately locate response personnel to respond quickly to emergencies throughout the Metrorail system. This is particularly challenging in sprawling and congested metropolitan areas like Washington, DC. The aim of this project is to support the WMATA Office of Emergency Preparedness (OEP) in determining appropriate geographic locations for response personnel with reduced response times to all areas of the Metrorail system. To that end, we developed a simulation model that evaluates response times to emergencies at WMATA Metrorail stations. The model relies on historical data of WMATA emergency incidents to generate probability distributions of incidents, and queries Google Maps application programming interface (API) using Python to provide responder travel times that account for the traffic at that time of day. The user inputs the proposed responder locations (one or several bases) and the tool outputs the response times to a set of emergencies. Resulting response times are then analyzed, visualized, and compared across scenarios, using response time distributions and geographic heat maps, to show response times for the system overall as well as specific stations or geographic areas. In collaboration with the WMATA OEP, we evaluate several scenarios involving moving their current OEP base to a more central location and/or allocating response personnel to different geographic areas. Based on these results, we recommend better locations for WMATA response personnel, which could improve response times by up to 27 minutes or 67% throughout the Metrorail system. While these results are specific to WMATA, the tool could be easily adapted to other public transit systems to support decisions on the location of emergency response personnel.
This paper presents a multi-criterion decision analysis approach to developing a procurement capacity index for local government units (LGUs) in the Philippines. The index serves to assess the resilience of LGUs in times of crisis, particularly in the context of the COVID-19 pandemic. This study utilized two open datasets published by the Philippine government from January to June 2020, and identified five criteria for the procurement capacity index: total approved budget of the contract, internal revenue allotment, number of awarded tenders, number of tenders posted, and fund utilization rate. This study then employed the criterion impact loss (CILOS) method to determine the weight vectors of the identified set of criteria, and calculate the index as a weighted sum based on these vectors. This study found that the fund utilization rate and internal revenue allotment are the two most important criteria for determining the capacity of an LGU to secure goods or services during a crisis such as the pandemic. This insight is consistent with observations drawn from use cases in the US, UK, and Canada as revealed in reviewed literature. Results also revealed that LGUs can be categorized into three clusters based on their procurement capacities: low, medium, and high. Moreover, the developed index facilitated the ranking of LGUs according to their procurement capacity, revealing that LGUs located in Regions II, III, VI, VII, VIII, and X have insufficient budget allocation, thus strongly suggesting urgent intervention from the national government. Overall, the developed index can serve as a valuable decision aid tool to assist the government in identifying LGUs that need additional support to procure resources or services required to mitigate the consequences of a crisis.
Recent advancements in unmanned aerial vehicles (UAVs), has allowed their deployment for numerous applications like aerial photography, infrastructure inspection, search and rescue, and surveillance. Despite the potential for full autonomy, many applications still necessitate human operators for navigating complex environments and decision-making. Existing solutions often employ high-precision and simple sensors like 2-D or 3-D LiDAR, which may provide more data than necessary and contribute to increased system complexity and cost. To address these challenges and bridge the gap between full autonomy and human-controlled UAVs, this work develops a shared-autonomy framework for UAVs, leveraging lightweight, low-cost 1-D LiDAR sensors combined with mobility behaviors to obtain performance comparable to more advanced 2-D/3-D LiDAR sensors while minimizing energy, computation overhead, and weight. Our framework includes a novel state machine method that exploits the UAV mobility to compensate for the limitations of 1-D LiDAR sensors, ensuring safety and obstacle avoidance through a physics-based algorithm that transitions between teleoperation and autonomous mode as needed based on environmental conditions and safety-critical issues. Experimental validations on real UAVs demonstrates the effectiveness of this shared autonomy scheme in complex environments, and the system is further generalized to larger UAVs and prototyped with a custom sensor configuration and onboard obstacle avoidance.
The most replaced valve in the congenital heart defects population is the pulmonary valve. Current treatments require multiple invasive, open-heart surgeries to replace the pulmonary valve as the patient grows. Minimally-invasive procedures, such as transcatheter pulmonary valve replacement, circumvent open heart surgery, which benefits patients and physicians. ePTFE valves have gained prominence recently because they allow cardiothoracic pediatric surgeons to tailor the valve size to the patient. Clinical studies point to the high patency of ePTFE valves. Currently, there are no FDA-approved transcatheter valves made with ePTFE. The purpose of this paper is to offer a preliminary design feasibility investigation of developing a transcatheter ePTFE pulmonary valve. We used an existing FDA-approved Medtronic Ensemble II delivery system to test and evaluate the feasibility of fitting an ePTFE pediatric valve to this existing deployment system. The prototyping and testing goals were: (1) to evaluate stent expansion, (2) to explore anchoring mechanisms, (3) to collapse the stent with the ePTFE conduit down to a size that would fit into a catheter, and (4) to expand and deploy the ePTFE stent-conduit pulmonary valve. Testing revealed that ePTFE conduits with wall thicknesses of 1 mm and 0.5 mm did not collapse to a small enough diameter to fit in the sheath of the Medtronic Ensemble II delivery system. However, expansion of anchored stents showed that valves upheld circular cross-sections when deployed. For the valve to be widely distributed, the product must follow FDA and ISO 5840 standards to ensure appropriate performance. There remains considerable future work to deliver a well-functioning ePTFE transcatheter pulmonary heart valve solution for pediatric patients.
Balance assessments are a common method of measuring vestibular and proprioceptive function as well as lower-body strength. Aside from observational clinician analysis of balance exercises, more detailed and conclusive assessments are typically performed using large, nonmobile, and expensive immersive systems. The purpose of this project is to replace existing balance testing equipment and provide an alterable environment for clinical postural control evaluation to enable development of personalized physical rehabilitation methods. To assess and train postural control, balance, and strength, this project incorporated real-time center of pressure data of a user on an on-floor force plate as the user completed a unique balance assessment in a Virtual Reality (VR) environment. Leaning or other movements altering the center of pressure location correspondingly caused movement through the VR environment. The VR environment was designed to assist ankle injury rehabilitation and included tasks to evaluate and compare mobility of the ankles. Quantitative measurements of 2-dimensional range of motion were coded to be recorded and coupled with clinician observational analysis for physical therapy applications. Lag between the force plate and VR device was minimal to prevent motion-sickness, and users could navigate through the VR environment, including tight areas, using planted sway movements with ease. This project developed novel physical rehabilitation methods using quantitative postural control analysis and can be further expanded upon to improve numerous physiological or vestibular conditions.
Due to the importance of environmental issues, customers prefer to buy low-carbon products and have an Ecofriendly behavior. Manufacturers produce the substitutable product under cap-and-trade regulations. Two chains are incompete on a product's green level, which is determined by the manufacturer. In some cases, firms have difficulties in providing sufficient capital to buy extra carbon emission quotas which force them to take loans from the banks. In this study, a two-echelon dual-channel supply chain consisting of one manufacturer and one retailer have been studied and a Stackelberg game is implemented on vertical and horizontal approaches. Various metaheuristic and hybrid metaheuristic methods are applied to optimize the revenue based on optimal decision variables such as retailer prices, carbon emission reduction rate, bank interest rate, and wholesale price. Performance of the applied methods are compared which determines the best algorithm in each case.
Sustainability and environmental ethics are major focuses of future developments in many fields of infrastructure and industry. One of these fields is the industry of garment dyeing. With only a handful of garment dyeing facilities in the country, TS Designs, located in Burlington, North Carolina, has developed a niche clientele and craft of the use of natural materials for use in commercial dyeing. Organic materials have been used in textile dyeing since the very beginning of documented history, but limited research has been done in the translation of these practices to industrial contexts. Natural dye can be derived from organic waste products and is a great way to incorporate eco-friendly methods in the industrial production of clothing. Unfortunately, due to the dyes being made from organic materials, the resulting color of the product may change over time as the material degrades, which is not preferable for the sale of a consistent product. It is important to extract dye from materials as they are available before they degrade in order to reduce waste. The goal of our research is to be able to test the dye stability of organic materials and determine proper practices for preserving each dye extract.
The human body follows a natural circadian rhythm, influencing sleep timing, cognitive abilities, and physical energy. Many people live contrary to this biological rhythm, leading to reduced cognitive performance and sleep loss, with college students especially vulnerable to these effects. Currently, there are limited technologies that assist with circadian rhythm alignment, despite the potential for health and productivity benefits. This paper investigates the feasibility of circadian-based activity scheduling for college students. We develop three circadian-based activity schedules that are increasingly personalized: (1) common activity timing according to circadian rhythms research, (2) timing curation according to sociodemographic context, and (3) timing adjustment based on individuals’ specific constraints and context. In a three-week study, we explore users’ responses to each scheduling approach and the potential impact on subjective wellbeing and overall performance. Our results show that participants could follow more activity recommendations as the level of personalization increased. Participants who followed the circadian schedules reported significantly improved well-being than others. However, reported wellbeing was not significantly correlated with increased personalization of timings. These observations provide useful insights into design requirements for circadian-aware recommendation systems.
Migrating an enterprise network to a cloud-based platform can help a company realize the benefits of increased automation, security, scalability, and usability. However, completing the migration can be tedious and time-consuming, so as to require an experienced network engineer. Many small and medium-sized enterprises do not internally employ such experts. Moreover, since cloud-based migration is only completed once, companies of all sizes opt to hire a professional network engineer as a consultant. During the migration process, companies often face communication challenges with the hired consultant. The work herein describes the design of a project management tool for cloud network migration to be used as an interface between the enterprise and network engineer. Its design was based upon extensive evaluation of information and functional requirements, and the establishment of the user flow. The design of the user interfaces realizes three important features: a task-based structure that centralizes resources, a graphical map for evaluating the status of dependent tasks, and embedded learning resources for furthering knowledge of networking. In this way, the design of the interface seeks to effectively bridge gaps in communication between enterprises and network engineers.
This capstone project aims to modify and finalize an existing hydroponic crop cultivation (HCC) system, called the "Fold-out-Farm," to operate on a floating platform in Small Island Developing States (SIDS) that are susceptible to food insecurity due to natural and economic factors. Specifically, when SIDS are hit by natural disasters, crops and agricultural infrastructure can be severely damaged, causing many people to suffer from a lack of both food access and job opportunity. The Fold-out-Farm is completely self-sufficient – it has its own water collection system, solar-based power generation, and on-board growing pods. The unit can float to combat disaster consequences from incidents such as hurricanes. Specifically, the project is working to add a rainwater harvesting system and validate the structural integrity of the unit during a flood. The farm is designed to use off-the-shelf nutrient solutions to grow a variety of crops and the team will find the most suitable option. The team will also expand the market niche for the HCC system by determining the optimal use for the product in urban food deserts, refugee camps, and rooftop gardens. The approach taken has involved communication and research to understand the needs of those who could benefit from a Fold-out-Farm, as well as various testing methods for crops and structure of the unit. Testing has been done through expert surveys, estimation of structural performance, simulation software analysis, and evaluation of crop yield from the unit relative to a control crop grown in soil. Results will be continuously measured, first in testing the system’s ability to deliver water, sun and nutrients to growing modules, its crop yield, and stability in an open water test in the Rivanna river, and finally when presenting the design to sponsors and potential users. Future researchers may build upon these findings to further improve the unit and its potential use to ensure that it is understandable and acceptable to the communities who will be using it. The project will have a market-ready product capable of reducing food insecurity in SIDS and potentially in urban food deserts, refugee camps and rooftop gardens in land scarce areas.
This paper leverages on the power of clustering algorithms to determine potential deficiencies in mathematics competencies of incoming first year engineering students who are graduates of the recently implemented K to 12 program. To achieve this objective, a total of 23 prerequisite mathematics competencies in Algebra, Advance Algebra, Plane and Spherical Trigonometry, Analytical Geometry, and Solid Mensuration common to engineering programs offered in the country were identified from the approved policies, standards, and guidelines published by the Commission on Higher Education. A survey instrument was developed to assess the self-rated proficiencies of participating respondents in these competencies using a 5-point liker scale (5 being the highest). Results of the clustering analysis showed the formation of four distinct groups of students based on their self-rated proficiencies: (1) above average, (2) average, (3) below average, and (4) poor. It was found that participants generally gave low self-ratings to Advanced Algebra and Analytic Geometry prerequisites. Although it is expected for non-STEM graduates of the K to 12 program to struggle in engineering programs, further analysis of the clusters revealed that about half of the STEM respondents self-rated their proficiencies in the below average and poor clusters. Though it may be argued that this finding may need to be explored further in the absence of ground truth labels, diagnostic examination scores garnered by the participants validate this observation. In conclusion, this study highlights the need for universities to implement targeted intervention programs to address the identified deficiencies and ensure the success of their enrollees in engineering programs.
Only 56% of eligible pediatric cardiac donor hearts are ultimately being accepted even though there are long waitlists for transplants and high waitlist mortality. A major contributing factor to low acceptance rates is due to the highly variable decision-making process of cardiologists who must determine the suitability of a potential transplant in an extremely short period of time. The current system, DonorNet, does not present information ideal for decision making under these conditions which has resulted in suboptimal decisions and cardiologists not being confident in their decisions. The goal of this project aims to adopt a user-centered systems design approach to develop a new DonorNet dashboard to better support the decision-making process for pediatric cardiologists. The design of an improved DonorNet dashboard was based on: (1) a literature review to understand the factors that influence practitioners in their decision-making process and identifying post hoc factors that are predictors of transplant success and (2) interviews by the research team with eight pediatric heart transplant practitioners to understand how end-users make decisions with DonorNet and identify common pain points. Based on this, we designed a dashboard using Figma based on our research findings that addressed identified pain points such as difficulty finding relevant data. We measured success with user satisfaction surveys before and after the redesign that included questions regarding how easy it was to find information and confidence in their decision. The expected results of the project will include a semi-functional dashboard that incorporates real data from databases containing information on patient and donor heart characteristics. Success of the interface will be evaluated through surveys assessing user satisfaction, time to arrive at a decision, and self-rated stress levels.
Artificial intelligence (AI) and machine learning (ML) are increasingly being used in cyber operations. Because of techniques like adversarial learning, the performance of network defenses can degrade quickly. Thus, there is an increasing need for adaptable, dynamic network defenses. Correspondingly, there has been a rise in the use of reconfiguration schemes like moving target defense in software-defined networks. However, moving target defense methods target individual adversaries and rely on an in-depth understanding of an adversary’s utility function. In contrast, domain adaptation theory suggests that learning agents are sensitive to distributional changes in their inputs, regardless of their utilities. In this paper, we identify several kinds of network changes that deter adversaries by exploiting vulnerabilities in their learned assumptions. We use an open source network attack simulator, NASim, to conduct experiments on reinforcement learning (RL)based penetration testers. We measure the time-to-relearn in order to compare the efficacy of different network changes at deterring adversaries. We find that by focusing on shifting the learning domain as a defensive strategy, we are able to degrade the performance of multiple adversaries simultaneously. With our methodology, cyber defenders have tools that allow them to raise the sophistication and cost needed by adversaries to remain a threat to network operations over time.
